NDLI: License Plate Recognition using multilayer neural networks

Content Provider	IEEE Xplore Digital Library
Author	Abdullah, S.N.H.S.
Copyright Year	2006
Abstract	Vehicle license plate recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image processing library is developed in-house which we referred to as vision system development platform (VSDP). The Kirsch edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of about 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.
Starting Page	1
Ending Page	7
File Size	7675157
Page Count	7
File Format	PDF
ISBN	9781424402199
DOI	10.1109/ICOCI.2006.5276525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-06
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Image processing Machine vision Neural networks Classification Licenses Multilayer perceptrons Feature extraction Libraries License plate recognition Multi-layer neural network Vehicles
Content Type	Text
Resource Type	Article

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